Georg Keilbar
Biographic Data
| ID | 8920856 |
|---|---|
| NAME | Georg Keilbar |
| GIVEN NAMES | Georg |
| FAMILY NAME | Keilbar |
| SIGNATURE | KEILBAR G |
| AFFILIATIONS | Humboldt-Universität zu Berlin |
| ORCID | 0000-0002-5574-3405 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Shapley Curves
This article fills the limited statistical understanding of Shapley values as a variable importance measure from a nonparametric (or smoothing) perspective.We introduce population-level Shapley curves to measure the true variable importance, determined by the conditional expectation function and the distribution of covariates.Having defined the estimand, we derive minimax convergence rates and asymptotic normality under general conditions for the…
Modelling systemic risk using neural network quantile regression
We propose a novel approach to calibrate the conditional value-at-risk (CoVaR) of financial institutions based on neural network quantile regression. Building on the estimation results, we model systemic risk spillover effects in a network context across banks by considering the marginal effects of the quantile regression procedure. An out-of-sample analysis shows great performance compared to a linear baseline specification, signifying the impor…
No prominent works on this page.
Modelling systemic risk using neural network quantile regression
We propose a novel approach to calibrate the conditional value-at-risk (CoVaR) of financial institutions based on neural network quantile regression. Building on the estimation results, we model systemic risk spillover effects in a network context across banks by considering the marginal effects of the quantile regression procedure. An out-of-sample analysis shows great performance compared to a linear baseline specification, signifying the impor…
Shapley Curves
This article fills the limited statistical understanding of Shapley values as a variable importance measure from a nonparametric (or smoothing) perspective.We introduce population-level Shapley curves to measure the true variable importance, determined by the conditional expectation function and the distribution of covariates.Having defined the estimand, we derive minimax convergence rates and asymptotic normality under general conditions for the…
Computer Science (2 works) · Econometrics (2 works) · Applied Mathematics (1 work) · Asymptotic distribution (1 work) · Complex Systems and Time Series Analysis (1 work) · Covariate (1 work) · Data mining (1 work) · Economic and Environmental Valuation (1 work) · Economics (1 work) · Estimator (1 work)